In this section we show the performance of our mesh segmentation algorithm FSS with several 3D triangulation models.
In the experiments reported in this section and in the next one, the kmeans ++ [1]} algorithm is applied to the
normalized rows of the rectangular matrix \(W^k\) , composed by the \(k\)  selected columns of \(W\) . These rows
are considered as points in \(\mathbb {S}^{k-1}\) . To measure the distance between two vectors we use the cosine distance, which
means that the distance is defined as one minus the cosine of the included angle between them. Several replicates of kmeans ++
algorithm are applied and for each replicate the seeds of the \(n_c\)  clusters are selected randomly. In general, the algorithm
FSS works very fast since in all segmentations \(k\)  is at most \(10\%\)  of the total number of faces. Our goal here
is just to evaluate visually the quality of the segmentations produced by the algorithm.
